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arXiv Machine Learning By Javad Komijani

Sampling SU(N) gauge theory on a 2D lattice from independent plaquettes via holonomies and corner reweighting

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The paper presents a method for sampling SU(N) gauge theory on a two‑dimensional lattice by independently generating plaquette holonomies and then reweighting configurations to satisfy consistency at lattice corners. By reformulating the Wilson action in terms of holonomy variables, the authors reduce the problem to sampling pairs of SU(N) matrices conditioned on their commutator, for which they derive closed‑form densities for SU(2) and SU(3). They demonstrate high per‑plaquette acceptance rates (>99%) and moderate effective sample sizes on both small (2×2) and large (32×32) lattices using normalizing flows and closed‑form samplers.

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